{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6418dce8-3ad0-4da9-81de-b3bf57956086",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from IPython.display import Markdown, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75b7849a-841b-4525-90b9-b9fd003516fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "headers = {\n",
    " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
    "}\n",
    "\n",
    "class Website:\n",
    "    def __init__(self, url):\n",
    "        self.url = url\n",
    "        response = requests.get(url, headers=headers)\n",
    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
    "        self.title = soup.title.string if soup.title else \"No title found\"\n",
    "        for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
    "            irrelevant.decompose()\n",
    "        self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45c07164-3276-47f3-8620-a5d0ca6a8d24",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
    "and provides a short summary, ignoring text that might be navigation related. \\\n",
    "Respond in markdown.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b334629a-cf2a-49fa-b198-edd73493720f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_prompt_for(website):\n",
    "    user_prompt = f\"You are looking at a website titled {website.title}\"\n",
    "    user_prompt += \"\\nThe contents of this website is as follows; \\\n",
    "please provide a short summary of this website in markdown. \\\n",
    "If it includes news or announcements, then summarize these too.\\n\\n\"\n",
    "    user_prompt += website.text\n",
    "    return user_prompt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4dd0855-302d-4423-9b8b-80c4bbb9ab31",
   "metadata": {},
   "outputs": [],
   "source": [
    "website = Website(\"https://cnn.com\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65c6cc43-a16a-4337-8c3d-4ab10ee0377a",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [\n",
    "    {\"role\": \"system\", \"content\": system_prompt},\n",
    "    {\"role\": \"user\", \"content\": user_prompt_for(website)}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59799f7b-a244-4572-9296-34e4b87ba026",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ollama\n",
    "\n",
    "MODEL = \"llama3.2\"\n",
    "response = ollama.chat(model=MODEL, messages=messages)\n",
    "print(response['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0c03050-60d2-4165-9d8a-27eb57455704",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
